graph TB subgraph "Frontend Layer" Frontend[Next.js Frontend] end subgraph "API Layer" API[FastAPI Backend] DR[Directory Router] TR[Train Router] TAGR[TagRouter] FR[File Router] SR[Search Router] end subgraph "Service Layer" ES[EmbeddingService] RS[RedisService] FS[FileService] TS[TrainingService] end subgraph "ML Layer" VJEPA[V-JEPA2 Models] CL[Classifier Training] INF[Inference Pipeline] end subgraph "Data Layer" Redis[(Redis DB)] Storage[File System] Models[Trained Models] end subgraph "Redis Storage" HNSW[HNSW Vector Indices] Meta[File/Directory Metadata] Tags[Tag Management] FTM[Fine-Tuned Models] end Frontend --> API API --> TR API --> FR API --> SR API --> DR API --> TAGR TR --> TS FR --> FS SR --> ES DR --> RS TAGR --> RS TS --> RS TS --> CL TS --> INF ES --> VJEPA ES --> RS FS --> RS CL --> Models INF --> VJEPA INF --> Models RS --> Redis FS --> Storage Redis --> HNSW Redis --> Meta Redis --> Tags Redis --> FTM 

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Directory Details

View detailed information and statistics for each directory

Files Gallery

Gallery view of all your media files with metadata

Similarity Search

Find similar images and videos using AI-powered vector search

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Feature Demonstrations

Viewing Local Files

Browse and navigate through your local media library

Similarity Search in Action

Search for visually similar content across your entire media library

Classifier Training

Train custom ML classifiers on your media collection

File Classification

Automatically classify and tag files using trained models

• Docker and Docker Compose
• Python 3.12+ (for virtual environment - venv)
• ffmpeg 6.1.1+
• make
• git

1. Clone the repository

   git clone https://github.com/emapco/ai-media-gallery.git cd ai-media-gallery

2. Install dependencies and build

   make install

   This will:

   • Create a Python virtual environment
   • Build Docker containers
   • Install Python and Node.js dependencies

   Optional GPU Optimization: If you have a GPU that supports Flash Attention, run:

   make install-flash-attn

   • Note that this requires CUDA Toolkit and may take a long time to build.

3. Start the application

   make start

   This starts:

   • Redis database on port 6379
   • Frontend on http://localhost:3000
   • FastAPI backend on http://localhost:7999

4. Stop the application

   make stop

The application can be configured using YAML configuration files in the conf/ directory:

• conf/backend.yaml - Backend service configuration including:

  • V-JEPA2 model settings and embedding parameters
  • HNSW vector index configuration for Redis
  • File service settings (thumbnails, supported formats)
  • Performance tuning (workers, batch sizes)

• conf/classifier.yaml - ML classifier training configuration including:

  • Model architecture and training hyperparameters
  • Data processing settings (video/image formats)
  • Training arguments (epochs, learning rate, optimization)

Development environment variables in .env:

# Redis Configuration REDIS_HOST=localhost REDIS_PORT=6379 REDIS_PORT_TESTING=6378 # Separate port for tests REDIS_PASSWORD= # Backend Configuration BACKEND_LOG_LEVEL=info BACKEND_WORKERS=2 BACKEND_HOST=localhost BACKEND_PORT=7999 BACKEND_BASE_URL=http://localhost:7999 # Frontend Configuration NEXT_PUBLIC_API_URL=http://localhost:7999 NODE_ENV=development

1. Install development dependencies

make install-dev

2. Start development server with hot reloading

make dev

This starts:

• Redis database with test data
• Frontend development server with hot reloading on http://localhost:3000
• FastAPI backend with auto-reload on http://localhost:7999

Code Quality

make format # Format Python (ruff) and TypeScript (prettier) code make lint # Run linting checks

Testing

make test # Run all tests (Python pytest + Jest) make cov # Run tests with coverage reports

API Development

make dev-api # Start API server with hot reloading make code-gen # Generate TypeScript API client from OpenAPI spec

• conf/ - Configuration files for backend and ML training
• src/ - Source code directory
  • app/ - Next.js frontend application
  • api/ - FastAPI backend with routers and services
  • components/ - React components for frontend
  • ml/ - Machine learning models and training scripts
  • utils/ - Frontend Utility functions, hooks, and types
• tests/ - Test files for both frontend and backend
• docker/ - Docker configuration files
• models/ - Trained ML models storage
• data/ - Redis data persistence